Table 7_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 9_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Analytical framework and statistical methods. by Na Chen (153323)

“…Our findings reveal significant variations in income insecurity and social protection responses across these groups. the pandemic had a significant impact on household incomes globally, with lower-middle-income countries experiencing the most significant income reductions. …”

Theoretical frameworks of social protection. by Na Chen (153323)

“…Our findings reveal significant variations in income insecurity and social protection responses across these groups. the pandemic had a significant impact on household incomes globally, with lower-middle-income countries experiencing the most significant income reductions. …”

Image 3_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Image 1_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Image 2_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Image 4_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Test plan. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting curve parameters. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Test instrument. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Empirical model establishment process. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Model prediction error trend chart. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Basic physical parameters of red clay. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

BP neural network structure diagram. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”